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Tag: social networks

Community

As someone who tried to build a fashion social network and is now an investor who sees his fair share of social networking startup ideas, I can attest to the difficulties in building a genuine community.

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So, when people question why Friendfeed users like myself are so dedicated to the site and why we don’t switch over to the new Facebook Groups feature (which has integrated many of Friendfeed’s features), I find myself scratching my forehead as to why so many web experts seem to miss out on the obvious.

The point so many web sites seem to misunderstand is that community is not a feature. If I got paid everytime someone said “we’ve added a ‘Post to Facebook’/‘bulletin board’/‘chat’/[insert other cliché “community” feature] feature” as evidence that they had a strong community, I would be a very wealthy man. To be fair, not having certain social features makes it harder to have a community, but having those features doesn’t necessarily mean you will have a community. You don’t add community to a website the way you might add Google Analytics or a new banner ad.

Community is something which has to be built and nurtured. At its core, its about users experiencing a genuine connection with other people and wanting to engage more: both on and off the site.

Similarly, community is not just having a lot number of users. Sure, Twitter/Facebook/LinkedIn have a ton of users. But, that alone doesn’t make them a community. Walmart has a lot of employees too – I doubt an outsider would consider that a tight-knit community.

What matters is not so much the number of users, but the number and quality of connections that they make. That’s one reason I actually consider the core group of Twitter users that I engage with a closer community than my LinkedIn or Facebook circle  (which is composed mostly of people that I actually know and have interacted with “in the real world”!) – I “talk with” (or Tweet) that group on Twitter more than I engage with people on Facebook, get a lot more value out of those internet relationships (I learn about interesting things, keep up with the daily actions of people I know, and get comments on things I share/say) than I do through those other sites. It doesn’t mean I don’t find LinkedIn or Facebook valuable (I do, for other reasons), but its that community which keeps me coming back and more engaged with Twitter, and Friendfeed for that matter, than with LinkedIn or Facebook.

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So, back to the original question – why do I stick with Friendfeed?

  • Bookmarklet: The FriendFeed bookmarklet is extremely powerful: its not only my primary means of sharing things on Twitter, it also lets me pull in additional content beyond Twitter’s 140 character limit. This convenience and pattern of use is difficult to break.
  • Feature set: There are practically zero features on Friendfeed which haven’t been replicated by someone else (esp. Facebook). However, I have yet to see the killer social feature which has convinced me to replace Friendfeed with something else – simply put, its good enough for what I need and, until it stops being good enough or I find something else far better, I’ll be sticking around.
  • Quality of Community: The people I engage with (and people-watch) on Friendfeed and the sorts of conversations that are had are deeper and more satisfying than almost any online forum I’ve been on (with the noteworthy exception of the group of friends I interact with on Google Reader). That exclusivity and depth of engagement is something I have yet to see Facebook or any other social media site replicate and, until they do and until the community that I like engaging with on Friendfeed chooses to move elsewhere, I don’t plan on stopping.

(Image credit) (Image credit)

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Science of Social Networks

Another month has gone by which means another paper to cover!

image This month, instead of covering my usual stomping grounds of biology or chemistry, I decided to look into something a little bit more related to my work in venture capital: social networks!

The power behind the social network concept goes beyond just the number of users. Facebook’s 500 million users is pretty damn compelling, but what brings it home is that by focusing on relationships between people rather than the people themselves, social networks turn into a very interesting channel for information consumption and influence.

This month’s paper (from Damon Centola at MIT Sloan) covered influence – specifically, how different social network structures (or “topologies” if you want to be snooty and academic about it) might have different influences on the people in the networks. More specifically, it asked the question of what social network would you expect to be better able to influence behavior: one which is more “viral”, in the sense that connections aren’t clustered (i.e., I’m as likely to be friends with my friend’s friends as people my friends don’t know), or one which is more “clustered” (i.e., my friends are likely to be friends with one another).

It’s an interesting question, and I found this paper notable for two reasons. First, its the most rigorous social networking experiment I’ve ever seen. Granted, this isn’t saying very much. Most social network/graph studies are observational, but I was impressed by the methodology and the attempt to strip out as much bias and extraneous factors as possible:

  • The behavior being tested was whether or not they would sign up and re-visit a particular health forum. This forum had to be valuable enough to get people to use it (and actually contribute to it), but also unknown and inaccessible to the rest of the world (as to avoid additional social cues from the user’s “real world” social network).
  • The author (and I do mean one single author: pretty rare these days for a Science paper as far as I know) created different social graphs which were superficially identical (same number of users, same number of contacts per user) but had the different network structures he wanted to test(one structure had subgroups of tightly inter-connected users, the other structure had random connections scattered across the network). The figure below shows one example of the network structures: the black lines show connections between people. On the left-hand-side is the highly clustered social graph – the individual users are only connected to people “next to them”. The right-hand-side is the more “viral” social graph, where users can be connected to any user across the social network.
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  • The users made profiles (with user name, avatar, and stated health interests), but to preserve anonymity (and limit the impact of a person’s “real world” social network on a user), the user names were blinded and users were not allowed to directly communicate (except in an anonymized fashion through the health forum) or add/remove contacts
  • However, whenever a user’s contacts participated in the health forum, the user would be notified.

The result was a somewhat bizarre and artificial “network” – but its certainly a very creative (and probably as good as it can get) means of turning social networking studies into a rigorous study with real controlled experiments.

Second, the conclusion is interesting and has many implications for people who want to use social networks to influence people. Virality may be a remarkably fast way to get people to hear about something, but the paper concludes that virality does not necessarily translate into people acting. The author conducted 6 different trials with slightly different network topologies (number of users ranged from 98 to 144, number of contacts per user ranged from 6 to 8). The results are in the graph below which shows the fraction of the users who joined the forum over time. As you can see, the clustered networks (solid circles) had much higher and faster adoption than the “viral” networks (open triangles):

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Why would this be? The author’s standing theory is that while “viral” networks might be faster at disseminating information (e.g., a funny video), clustered networks work better at driving behavior because you get more reinforcement from your friends. In a clustered network, if you have one friend join the forum, chances are the two of you will have a mutual friend who will also join. At a very basic level, this means you get the same cue to join the forum from two of your friends. In a un-clustered network, however, if you have one friend join the forum, the two of you are less likely to have a mutual friend, and so you are less likely to receive that second cue.

Does this matter? According to the study, someone who had two contacts join the forum was ~75% more likely to join than someone who only had one contact join. And, someone who had four contacts join was ~150% more likely to join than someone who only had one contact. While this effect rapidly diminshes with more contacts (having five or six contacts join made relatively little difference compared with four), its a powerful illustration of quality vs. speed in a social network – something which is also borne out by the fact that while only 15% of people who only had one contact join returned to the forum, 35-45% of users who had multiple contacts join did.

This was definitely a very impressive and well-designed study. While it would be fair to attack the study for its artificiality, I don’t really think there’s any other way to systematically strip out the  biases that are intrinsic to most observational (not a controlled experiment) studies of social networks.

Where I do think this was lacking (and maybe the researcher has already teed this up) is the black-and-white nature of the study. What I mean by this is while I find the argument that network clustering helps drive greater behavior plausible, I think there needs to be a more rigorous/mathematical conception – how “clustered” does a network need to be? If a network is overly clustered, then it loses the virality which helps to spread ideas more quickly and widely – is there an optimal balance somewhere in the middle? Also, the paper only dug, on a very superficial level, into how network size and the number of contacts per user might impact this. I think further experimental and mathematical modeling/computational studies would be nice to really flesh this out.

Paper: Centola, Damon. “The Spread of Behavior in an Online Social Network Experiment.” Science 329 (Sep 2010) – doi:10.1126/science.1185231

(Image credit – social network diagram) (Figures 1 and 2 from paper)

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Private concerns

imageOne reason I love science fiction is that it challenges our morals and beliefs in a way that other art forms rarely do. It asks us difficult questions, like, what if we had the ability to visit other planets and encounter different cultures? What if we could genetically “design” our children? What if we could go back in time and change history?

Unsettling questions aren’t they? But, why are they unsettling? My personal belief is that they are unsettling because our intuitions, our values, our beliefs, our laws, and our institutions were not designed to handle those questions. If you assume that Western culture is heavily derived from Ancient Greek and Roman humanism, is it any wonder that society has trouble understanding what to do with our nuclear arsenals or with humankind’s new ability to genetically alter the people and animals around us? After all, the foundations of today’s laws and values predated when people could even conceive that humans would ever have to think about such things.

So, when people ask me what I think about all the press that privacy concerns about Google or privacy concerns about Facebook or any of the other myriad social networks have garnered, I view it as manifestation of the fact that we now have technology which makes it super-easy to share information about ourselves and our location but we have yet to develop the intuititions, values, and laws/institutions to handle it.

Lets use myself as an example: I personally find auto-GPS-tagging my Tweets to be oversharing. However, I frequently Tweet the location I’m at and even the friends I’m with. Is this odd combination of preferences an example of irrationality? Probably (I was never the brightest kid). But I’d argue its more about my lack of intuition on the technology and the lack of clear cultural norms/values.

image And I’m not the only one who is beginning to come to terms with the un-intuitiveness of our digital lives. My good friend, and prominent blogger, Serena Wu recently went through a social network consolidation/privacy overhaul as a result of understanding just what it was she was sharing and how it could be used. All across the internet, I believe users are beginning to understand the consequences to privacy of their social network and search engine behavior.

Now, the easy reflex thing to do would be to simply cut off such privacy issues and cut out these social networks like one would a tumor. But, I think that would be a dramatic over-reaction akin to how the Luddites reacted to factory automation. It ignores the potential value of the technology: in the case of sharing information on social networks, this can come in the form of helping people advertise themselves to employers, assisting friends with keeping in contact with one another, and/or even delivering more valuable services over the internet. Now, that shouldn’t be construed as a blanket defense of everything Facebook or Twitter or Google does, but an understanding that there is a tradeoff to be made between privacy and service value is necessary to help the services, their users, society, and the government realize the appropriate changes in intuition, values, and rules to properly cope.

I’m not smart enough to predict what that tradeoff will look like or how our intuitions and values may change in the future, but I do think we can count on a few things happening:

  • Privacy will remain a big issue. Facebook and Twitter’s early years were marked by a very laissez-faire approach by both the users and the services on privacy. I believe that such an approach is unlikely to persist given the potential dangers and users’ growing appreciation for them. There is no doubt in my mind that, whether it be through laws, user demand, advocacy groups, or some combination of the above, data privacy and security will be a “must-have” feature of great significance for future web services built around sharing/accessing information.
  • Privacy policies and settings will become more standardized. I believe that the industry, in an attempt to become more transparent to their users and to avoid some of the un-intuitiveness that I described above, will build simpler and more standardized privacy controls. This isn’t to say that there won’t be room for extra innovation around privacy settings, but I think a “lexicon” of terms and settings will emerge which most services will have to support to gain user trust.
  • Data access APIs will become more restricted and/or use better authentication. The proliferation of web APIs has created a huge boom in new web services and mashups. However, many of these APIs use antiquated methods of authentication which don’t necessarily protect privacy. Consequently, I believe that the APIs that many new web services have grown to use will face new pressures to authenticate properly and frequently as to avoid data privacy compromise.

In the meantime, the few tips I listed below will probably be relevant to users regardless of how our rules, values, and intuitions change:

  • Understand the privacy policy of the services you use.
  • Figure out what you are willing to share and with whom as well as what you are not willing to share. Only use services which allow you to set access restrictions to those limits.
  • Check with your web service regularly on what information is being stored and what information is being accessed by a third party. (i.e., the Google Dashboard or Twitter’s Connections)
  • Advocate for better forms of authentication and privacy controls

No matter what happens in the web service privacy area, we are definitely in for an interesting ride!

(Image credit – ethics) (Image credit – Big Facebook Brother)

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Keep your enemies closer

One of the most interesting things about technology strategy is that the lines of competition between different businesses is always blurry. Don’t believe me? Ask yourself this, would anyone 10 years ago have predicted that:

I’m betting not too many people saw these coming. Well, a short while ago, the New York Times Tech Blog decided to chart some of this out, highlighting how the boundaries between some of the big tech giants out there (Google, Microsoft, Apple, and Yahoo) are blurring:

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Its an oversimplification of the complexity and the economics of each of these business moves, but its still a very useful depiction of how tech companies wage war: they keep their enemies so close that they eventually imitate their business models.

(Chart credit)

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